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ToDo Object Detection

  • Visualize some images with GT boxes, SS boxes and EB boxes, to understand the poor recall/MABO.

  • Play around with input parameters for edge boxes and SS, since our recall/MABO is not very impressive right now. Most easily done like in the section 'edge boxes' in test_oe.ipynb (should probably be moved to HPC for speed though). Should probably reduce to a subset of images so that different parameters can easily be tested

  • Make validation set and change code to use it

  • Create a test loop and implement some score functions

  • Start training seriously, and visualize results to see if we can improve anything. Stuff to play around with

    • Weighting between Classification Loss and Regression loss
    • Consider smooth L1 Hubert loss for BBOX regression (suggested by ChatGPT)
    • Thresholds for background/foreground IOU at train and test time

TASKS

  • Implement AP (Ø)

  • Clean up dataloader for RAM (Frederik) ---DONE ---

  • Try to understand what goes wrong in Loss/train/main (Mads)

  • Make "different steps"-plot that we can reference. Ideas for steps (probably don't include all): Initial image --> Image with GT --> Image with proposals --> Proposals classified and adjusted --> Final detection after NMS (Alex)

  • Make architecture flow chart graphic (Image -> proposal generator -> stack of proposals -> efficient net w two heads -> classification + t-values

  • Make plot of recall/mabo (Ø)

  • Make plot of train/validation loss history (Alex)

  • Make final test plots given a model (Ø)

  • Train some selected models for a long time and save detailed loss history, including accuracy. Maybe IOU before and after bbox adjustment?

Stuff to include on the poster

  • EfficientNet architecture
  • A NICE graph of Recall/MABO vs proposals-included. There should probably be one for SS and one for EB. Could include a few different thresholds in each plot for the recall (MABO is threshold-independent)
  • A viualization of an image (train) with boxes on top (GT boxes, and a subset of other options (foreground/background, according to IOU/model, SS/EB, adjusted with bbox regression or not))
  • A visualization of an image (test) with boxes on top?
  • Loss history for classification and regression
  • Consider making a flow chart of everything that goes on at test time (images goes into SS -> bboxes... Each bbox goes through Network... boxes are adjusted according to bbox regression... NMS... etc)

Schematic:

  • Introduktion

    • VERY short
    • Dataset (maybe image with the different steps)

  • Arkitektur (figur)

    • Proposal Generation
    • EfficientNet m. to heads, t_vals

  • Proposal generation

    • plot of recall/mabo (think about reordering proposals)
    • Show some examples of proposals

  • Training approach

    • Class balance dataloader
    • Loss choice (lambda)
    • Infobox on training: Loss, optimizer, epochs (in plot), stratification…

  • Results

    • Train/validation loss history. Maybe have for regression, classification, but definitely have sum. Include also classification accuracy
    • AP history or final AP table
    • All positive detections in an example image (maybe before and after NMS)
    • Afterwards, show same results on test data (maybe not even needed for validation)

Should we have? - Data Augmentation (part of ‘story’, if necessary) - Example of generated/selected proposals? - Loss history, AP history… - Confusion matrix on validation set (pure classification) - Confusion matrix on validation set or test set (including proposal generation, NMS). Show AP

Questions

  • What recall/MABO is acceptable?
  • Is overall stratified sampling ok, or should it be per-batch?
  • How should we sub-select proposals from SS and EB? Apply the method also when plotting Recall/MABO. I wrote about an idea in test_oe
  • Should we create our own CNN
  • Can we 'flip' the presentation, i.e. start with results
    • Brief answer: depends on the lecturer, but always start with task definition ('we want to solve this task'. Maybe one full sentence)